Legal claims defining the scope of protection, as filed with the USPTO.
4. The computer-implemented method of claim 1, wherein the ML training code is written in the Python language.
5. The computer-implemented method of claim 4, wherein the ML training code utilizes a TensorFlow deep learning framework or an XGBoost algorithm.
6. The computer-implemented method of claim 1, wherein the one or more containers are Docker containers.
7. The computer-implemented method of claim 1, wherein each of the one or more containers is executed by a virtual machine instance.
8. The computer-implemented method of claim 1, wherein the running of the ML model training job occurs based on use of a user-provided value indicating a role or set of permissions to be used for accessing resources within the service provider network.
10. The system of claim 9, wherein the ML training code is written in the Python language.
11. The system of claim 4, wherein the ML training code utilizes a TensorFlow deep learning framework or an XGBoost algorithm.
14. The system of claim 9, wherein the running of the ML model training job occurs based on use of a user-provided value indicating a role or set of permissions to be used for accessing resources within the service provider network.
18. The one or more non-transitory computer-readable media of claim 15, wherein the ML training code is written in the Python language.
19. The one or more non-transitory computer-readable media of claim 18, wherein the ML training code utilizes a TensorFlow deep learning framework or an XGBoost algorithm.
20. The one or more non-transitory computer-readable media of claim 15, wherein the one or more containers are Docker containers.
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January 10, 2023
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